Integrating machine learning into business and management in the age of artificial intelligence

The co-occurrence map of keywords, illustrated in Fig. 3, shows keywords as circles (nodes) and their correlations as curved lines (arcs). A correlation between keywords is indicated when a pair of keywords appears together in at least one retrieved paper (co-occurrence). The literature is organized into 15 distinct clusters: (1) financial markets assessment and forecasting; (2) time-series forecasting; (3) complex network analysis; (4) marketing and sales optimization; (5) digital transformation and sustainability; (6) strategic decision making; (7) education and skills development; (8) opinion mining and Electronic Word of Mouth (eWOM) analytics; (9) innovation and entrepreneurship; (10) explainable AI and public policy support; (11) logistics management; (12) geographic information systems (GIS) applications in business and management; (13) risk assessment and management; (14) data management and knowledge discovery; and (15) challenges in data analysis due to the COVID-19 pandemic.

C1: Financial markets’ assessment and forecasting

Figure 4a explores Cluster 1 (C1), focusing on the application of ML techniques in evaluating and predicting financial assets and market performance. Prominent ML algorithms in this cluster include Linear discriminant analysis (LDA), latent class analyses, and least absolute shrinkage and selection operator (LASSO), which have increasingly supplanted traditional econometric methods such as autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), and Gaussian mixture models (GMM) (Ghiassi et al. 2005; Pai and Lin, 2005; Ahmed et al. 2010; Mullainathan and Spiess, 2017).

Recent developments, especially in cryptocurrency markets like Ethereum and Bitcoin, have seen ML applied to forecast short-term dynamics of these volatile assets, leveraging both historical and real-time data (Urquhart, 2017; Zhang et al. 2018). A notable application is in predicting stock behaviours for bootstrapped enterprises, aiding in portfolio optimization and investment strategies (Manela and Moreira, 2017; Gu et al. 2020).

C2: Time-series forecasting

Figure 4b provides an overview of Cluster 2 (C2), which centres on ML techniques for time-series forecasting. This cluster is pivotal in predicting short-term fluctuations in business metrics such as financial KPIs, including sales and stock prices, and is influenced by macroeconomic variables like unemployment, inflation, and exchange rates (Ghiassi et al. 2005; Ahmed et al. 2010; Ferreira et al. 2016; Manela and Moreira, 2017; Mullainathan and Spiess, 2017; Vidya and Prabheesh, 2020).

Key ML frameworks in this area include long short-term memory (LSTM), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), which have gained traction for their ability to handle complex temporal data (Law et al. 2019; Fotiadis et al. 2021). GANs, in particular, are noted for their effectiveness in anomaly detection within time series data (Geiger et al. 2020; Zhang et al. 2021). Additionally, traditional algorithms such as particle swarm optimization (PSO), support-vector regression (SVR), ant colony optimization (ACO), and genetic algorithms (GAs) continue to be vital for addressing a wide range of time-series forecasting (Ahmed et al. 2010).

C3: Complex network(ing) analysis

Figure 4c illustrates Cluster 3 (C3), which is primarily focused on complex network analysis. This cluster involves ML techniques rooted in graph theory, including, graph neural networks (GNNs), convolutional neural networks (CNNs) applied to graphs, as well as clustering methods like density-based, grid-based, and affinity propagation clustering.

These methods are applied across diverse areas such as social network analysis (both real-world and online), collaborative filtering, and recommendation systems tailored for e-businesses and e-commerce. In particular, these algorithms support applications like link prediction, user modelling, and adaptive learning, making them crucial for enhancing community-driven recommendations in e-commerce (Wei et al. 2012). Additionally, this cluster includes research into fraud detection using ML techniques and document clustering (Dou et al. 2020).

C4: Marketing and sales optimization

Cluster 4 (C4), depicted in Fig. 5a, focuses on the application of ML in marketing and sales optimization. This cluster centres on the discovery and analysis of market segments and consumer profiles, leveraging clustering techniques and algorithms such as K-means, fuzzy C-means, and hierarchical clustering methods (Larson et al. 2005). These ML algorithms enhance customer relationship management (CRM) processes by enabling more precise segmentation and profiling of customers.

Within the domain of Segmentation, ML is employed to improve business-to-customer (B2C) and business-to-business (B2B) relationships (McCarty and Hastak, 2007; Paschen et al. 2020a). Techniques like K-means clustering and Gaussian mixture models facilitate the customization of high-value-added products and services tailored to specific customer needs, thereby enhancing customer satisfaction and loyalty (Syam and Sharma, 2018). Additionally, ML algorithms support the provision of supplementary services that elevate the overall customer experience.

Another significant application within this cluster is the use of ML for personalized advertising on social media platforms and Google Ads. By leveraging customer profiling through algorithms such as logistic regression, decision trees, and random forests, businesses can deliver more personalized and effective marketing strategies (Lee et al. 2018).

The banking industry prominently utilizes clustering algorithms for customer segmentation and service targeting, distinguishing its ML applications from other sectors that focus on areas like risk assessment, asset management, and stock market analysis (e.g., clusters C1 and C13).

Within C4, ML clustering techniques also complement analytic hierarchical process (AHP), multi-criteria decision-making (MCDM), and decision support systems (DSS) in strategic management. ML algorithms enhance the segmentation, targeting, and positioning (STP) process in marketing by providing data-driven insights that inform more effective segmentation and targeting strategies (Huang and Rust, 2021).

C5: Digital transformation and sustainability

Cluster 5 (C5), illustrated in Fig. 5b, centres on Digital Transformation and Sustainability within the context of Big Data analytics and Business Intelligence (BI). This cluster emphasizes the pivotal role of data-driven decision-making processes in driving enterprise digital transformation (Zaki 2019; Paschen et al. 2020a).

From a management perspective, digital transformation encompasses various Information and Communication Technologies (ICTs) and strategic concepts, including privacy preservation, data analytics, information security, blockchain, and the Internet of Things (IoT). While specific ML algorithms are not the primary focus of this cluster, ML plays a supportive role in several areas. For instance, classification and regression models are used to analyse vast datasets, enabling businesses to derive actionable insights and foster sustainable development (Fuchs et al. 2014; Tabesh et al. 2019; Paschen et al. 2020a). In sustainability efforts, ML contributes through predictive analytics and optimization algorithms that help in creating environmentally conscious business models. Techniques such as regression analysis, time-series forecasting, and unsupervised learning algorithms like principal component analysis (PCA) and clustering are employed to identify patterns and trends that inform sustainable practices (Goralski and Tan 2020; Di Vaio et al. 2020).

Cluster 5 involves high-level strategic discussions on sustainable digital transformation, with ML providing tools and methodologies that enhance data-driven decision-making and support the development of sustainable business models (Zaki 2019; Canhoto and Clear 2020; Di Vaio et al. 2020).

C6: Strategic decision making

The sixth cluster (C6), illustrated in Fig. 5c, delves into strategic decision-making, with a strong emphasis on classification and decision trees. This cluster highlights the use of ML decision tree algorithms and frameworks, including gradient boosting, random forests, and ensemble learning methods, which are pivotal in strategic management contexts (Samoilenko and Osei-Bryson, 2013).

In addition to decision tree-based algorithms, classification algorithms such as support vector machines (SVMs), Naïve Bayes, and K-nearest neighbours (KNNs) are extensively employed for strategic decision-making purposes. As defined by some scholars (Ferreira et al. 2016; Bertsimas and Kallus, 2020) these ML methods are instrumental in predictive scenarios, including:

• Forecasting financial churn and distress: Using logistic regression and SVMs to predict customer churn and financial distress.

• Evaluating bankruptcy possibilities: Applying random forests and gradient boosting to assess the likelihood of bankruptcy.

• Identifying failures: Utilizing KNNs and Naïve Bayes classifiers to detect potential failures within business processes or strategies.

These ML algorithms provide robust frameworks for making informed strategic decisions by analysing complex datasets and uncovering patterns that might not be apparent through traditional analytical methods.

C7: Education and skills development

The seventh cluster (C7), depicted in Fig. 6a, focuses on education and skills development. Enterprises are increasingly adopting ML-driven techniques and frameworks for online learning, particularly in training and onboarding procedures. Reinforcement learning algorithms and predictive analytics models are commonly used to personalize learning experiences, enhance assessment processes, and optimize retention rates. These ML methods facilitate performance measurement and increase engagement among trainees and newly onboarded employees, thereby improving the overall onboarding and skills development process (Bakhshinategh et al. 2018; Hellas et al. 2018; Fernandes et al. 2019).

C8: Opinion mining and electronic Word of Mouth (eWOM) analytics

Cluster 8, depicted in Fig. 6b, revolves around opinion mining and electronic Word of Mouth (eWOM) analytics. The maturation of natural language processing (NLP) algorithms has significantly advanced the automated analysis of user interactions on websites, e-commerce platforms, and news reports (Fuchs et al. 2014; Manela and Moreira, 2017; Guo et al. 2017).

ML frameworks for opinion mining frequently utilize algorithms like latent Dirichlet allocation (LDA) for topic modelling and bidirectional encoder representations from transformers (BERT) or Word2Vec for word embeddings. These techniques enable the classification of opinions into pre-defined categories, allowing for the analysis of large volumes of social media content with minimal human intervention (Xiang et al. 2017; Guo et al. 2017; Hartmann et al. 2019). Additionally, content analysis and semantic analysis approaches are enhanced by computer cision techniques, such as image clustering and object detection, to automatically review multimedia content (Ma et al. 2018; Zhang et al. 2019).

C9: Innovation and entrepreneurship

Figure 6c highlights Cluster 9, which centres on Innovation and Entrepreneurial opportunities driven by ML. Although this cluster primarily engages in high-level strategic discourse, without extensive practical applications of ML, there are key areas where ML plays a role. In Financial Technology (FINTECH), enterprises leverage predictive analytics, machine learning-based forecasting models, and decision support systems to navigate rapidly changing market conditions and to innovate in business strategies (Paschen et al. 2020b; Gu et al. 2020).

The use of ML in innovation is also discussed in the context of user experience (UX) design tools (Dove et al. 2017), where algorithms such as reinforcement learning (Rojas-Córdova et al. 2020) and recommendation systems (Graham and Bonner 2022) could potentially enhance user engagement and product development processes. While the exact applications of ML in entrepreneurship remain somewhat ambiguous, ongoing research suggests that ML could play a pivotal role in understanding and forecasting innovation trends and entrepreneurial success.

C10: Explainable AI and public policy support

The tenth cluster (C10), depicted in Fig. 7a, focuses on Explainable AI (XAI) and its critical role in supporting public policies through ML. Researchers in this cluster have developed frameworks to design ML algorithms that are transparent, fair, and high-performing. Key algorithms include SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME), which help in making ML models more interpretable and understandable by policymakers and the public (Adadi and Berrada, 2018; Preece, 2018; Miller, 2019; Barredo Arrieta et al. 2020). Mitigating biases in ML algorithms is central to XAI, as biases can contribute to public scepticism and distrust in data-driven decision-making. This concern extends to various sectors, including businesses, where biases impact employees and stakeholders (Kaplan and Haenlein, 2020; Paschen et al. 2020b). The ethical implications of ML algorithms, particularly regarding the potential reinforcement of biases against marginalized groups, have led to discussions on the responsible use of ML. Responses to these ethical challenges include regulatory frameworks like the European Union’s General Data Protection Regulation (GDPR) and the ongoing development of XAI methods (Preece, 2018; Kaplan and Haenlein, 2020; Canhoto and Clear, 2020).

C11: Logistics management

The eleventh cluster (C11), illustrated in Fig. 7b, examines ML applications in logistics management, emphasising the optimisation of operations and cost reduction. Key ML frameworks in this cluster include deep reinforcement learning and multi-agent systems, which are applied to solve complex logistics problems such as vehicle routing problems (VRP) and travelling salesman problems (TSP) (Chen and Wu, 2005; Luo et al. 2009; Ferreira et al. 2016).

Traditional optimization methods like simulated annealing and Tabu search are increasingly being complemented or replaced by advanced ML techniques, including spatial–temporal clustering and debiased ML. These methods are critical in supply chain management (SCM) tasks, such as identifying optimal delivery routes, forecasting product demand, agile supplier selection, dynamic pricing, and scheduling. By incorporating ML, businesses can enhance their logistics efficiency and reduce operational costs (Huang et al. 2011).

C12: Geographic information systems (GIS) applications in business and management

Cluster 12 (C12), depicted in Fig. 7c, is dedicated to the integration of geographic information systems (GIS) with ML techniques to enhance business and management applications. This cluster includes the use of spatial clustering and spatial data mining algorithms to develop and coordinate multimodal supply routes, assess housing prices for optimal location selection, and re-evaluate business investment values (Grekousis et al. 2013; Chen and Tsai, 2016). Beyond traditional GIS applications, this cluster explores the impact of criminal and gang activity, traffic patterns, and real estate dynamics on business operations. By integrating GIS with ML, such as K-means clustering and density-based spatial clustering of applications with noise (DBSCAN), businesses gain valuable insights that inform decision-making and improve operational efficiency. These ML-driven GIS applications are crucial for strategic planning, including workforce performance management, recruitment, onboarding processes, and asset valuation (Morency et al. 2007; Chang et al. 2010; Grekousis et al. 2013).

C13: Risk assessment and management

Cluster 13 (C13), depicted in Fig. 8a, focuses on risk assessment and management, particularly within the financial sector. ML plays a critical role in this domain, especially in assessing customer credit risk and managing debt risks. Traditional methods, such as Monte Carlo simulations, are increasingly being complemented by advanced ML algorithms like random forests, support vector machines (SVMs), and neural networks (Khandani et al. 2010; Bekhet and Eletter, 2014; Koutanaei et al. 2015).

This cluster extends beyond financial risks, encompassing business risks in project management, fraud detection, auditing, and investments, particularly in emerging economies. ML-driven techniques such as anomaly detection and predictive analytics are widely used in these areas to identify and mitigate potential risks (Thiprungsri and Vasarhelyi, 2011; Jin and Zhang, 2011; Gray and Debreceny, 2014). The FINTECH industry’s influence is notable, relying heavily on ML and ICTs to offer innovative financial services, although specific ML applications within FINTECH are often intertwined with those in clusters like C1 and C2 (Kalinic et al. 2019; Gu et al. 2020).

C14: Data management and knowledge discovery

The cluster depicted in Fig. 8b (C14) focuses on data management and knowledge discovery, a critical area for ML applications in business. This cluster highlights the processes involved in acquiring, preprocessing, and analysing data to maximize business value. The cross-industry standard process for data mining (CRISP-DM) remains the dominant framework in this area, guiding the knowledge discovery in databases (KDD) process (Fernandes et al. 2019).

ML techniques central to this cluster include constrained clustering, functional clustering, restricted Boltzmann machines, latent semantic indexing (LSI), and a priori algorithms. These algorithms are applied to continuous data streams from sources like web content, surveys, and market baskets, enabling businesses to perform tasks such as data cleaning, outlier detection, and data imputation (Chen and Wu, 2005). Additionally, non-ML techniques like linear regressions, Markov chains, and conceptual modelling complement these ML methods to ensure data quality and privacy in business operations.

C15: Challenges in data analysis brought by the COVID-19 pandemic

The last cluster, C15, centres on the challenges in data analysis brought by the COVID-19 pandemic, as depicted in Fig. 8c. ML algorithms have been pivotal in predicting the pandemic’s impact on businesses, with models like time-series forecasting and Bayesian networks being employed to assess the effects of lockdowns and restrictions (Vidya and Prabheesh, 2020; Baek et al. 2020; Fotiadis et al. 2021).

The pandemic has accelerated digital transformation across various industries, pushing businesses towards extensive digitization and the adoption of remote work policies. These changes have created new opportunities for ML applications in digital transformation projects, particularly those detailed in Cluster C5. Semantic networks and meta-analysis techniques, along with bibliometric analysis, have become essential tools for businesses navigating this new landscape, facilitating their transition to more digital operations (Sedera et al. 2022; Miklosik and Krah, 2023).